The past three decades have seen rapid development in the area of model predic-
tive control with respect to both theoretical and application aspects. Over these
30 years, model predictive control for linear systems has been widely applied,
especially in the area of process control. However, today’s applications often
require driving the process over a wide region and close to the boundaries of op-
erability, while satisfying constraints and achieving near-optimal performance.
Consequently, the application of linear control methods does not always lead to
satisfactory performance, and here nonlinear methods must be employed. This
is one of the reasons why nonlinear model predictive control (NMPC) has en-
joyed significant attention over the past years, with a number of recent advances
on both the theoretical and application frontier. Additionally, the widespread
availability and steadily increasing power of today’s computers, as well as the
development of specially tailored numerical solution methods for NMPC, bring
the practical applicability of NMPC within reach even for very fast systems. This
has led to a series of new, exciting developments, along with new challenges in
the area of NMPC.